Machine learning-based estimation of CO2 footprint and environmental-mechanical performance of blended cement concrete
Blended cements with supplementary cementitious materials (SCMs) are extensively used to mitigate the environmental impact of concrete. However, assessing their environmental performance often requires detailed data and time-intensive analyses. This study presents a machine learning-based methodolog...
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Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S221450952500539X |
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| author | Felipe Vargas Iván La Fé-Perdomo Jorge A. Ramos-Grez Ivan Navarrete |
| author_facet | Felipe Vargas Iván La Fé-Perdomo Jorge A. Ramos-Grez Ivan Navarrete |
| author_sort | Felipe Vargas |
| collection | DOAJ |
| description | Blended cements with supplementary cementitious materials (SCMs) are extensively used to mitigate the environmental impact of concrete. However, assessing their environmental performance often requires detailed data and time-intensive analyses. This study presents a machine learning-based methodology for the rapid estimation of the CO₂ footprint and environmental-mechanical performance (CO₂/MPa ratio) of concrete mixtures using only the proportions of their main components (i.e., ordinary portland cement (OPC), SCMs, aggregates, water, and water-reducing admixtures). The models were developed using a dataset of 246 mixtures compiled from the literature and validated against 15 experimentally tested mixtures. The results demonstrate that the Gaussian Process Regressor provides the highest predictive accuracy for both CO₂ footprint and CO₂/MPa ratio. Feature analysis revealed that OPC content has the highest impact on the CO₂ footprint, while aggregate fraction has the most significant influence on the CO₂/MPa ratio. An optimization framework was also implemented to explore the trade-offs among mix components, showing that increasing SCM content does not always lead to improved CO₂/MPa ratio, highlighting the need for balanced mixture design. The developed models offer a practical tool for supporting early-stage decision-making in construction projects by enabling rapid sustainability assessments of concrete mixtures, independent of specific SCM types, production methods, or geographical context. |
| format | Article |
| id | doaj-art-7a96347fbfae4c45bbd202dc2975fb18 |
| institution | OA Journals |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-7a96347fbfae4c45bbd202dc2975fb182025-08-20T02:27:15ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e0474110.1016/j.cscm.2025.e04741Machine learning-based estimation of CO2 footprint and environmental-mechanical performance of blended cement concreteFelipe Vargas0Iván La Fé-Perdomo1Jorge A. Ramos-Grez2Ivan Navarrete3Institute of Civil Engineering, Austral University of Chile, General Lagos 2086, Valdivia, ChileEscuela de Ingeniería Mecánica, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, ChileDepartment of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Macul, Santiago, Chile; Research Center for Nanotechnology and Advanced Materials (CIEN-UC), Vicuña Mackenna 4860, Macul, Santiago, ChileDepartment of Construction Engineering and Management, School of Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Macul, Santiago, Chile; Concrete Innovation Hub UC (CIHUC), Pontificia Universidad Catolica de Chile, Vicuña Mackenna 4860, Macul, Santiago, Chile; Corresponding author at: Department of Construction Engineering and Management, School of Engineering, Pontificia Universidad Católica de Chile, Vicuña Mackenna 4860, Macul, Santiago, Chile.Blended cements with supplementary cementitious materials (SCMs) are extensively used to mitigate the environmental impact of concrete. However, assessing their environmental performance often requires detailed data and time-intensive analyses. This study presents a machine learning-based methodology for the rapid estimation of the CO₂ footprint and environmental-mechanical performance (CO₂/MPa ratio) of concrete mixtures using only the proportions of their main components (i.e., ordinary portland cement (OPC), SCMs, aggregates, water, and water-reducing admixtures). The models were developed using a dataset of 246 mixtures compiled from the literature and validated against 15 experimentally tested mixtures. The results demonstrate that the Gaussian Process Regressor provides the highest predictive accuracy for both CO₂ footprint and CO₂/MPa ratio. Feature analysis revealed that OPC content has the highest impact on the CO₂ footprint, while aggregate fraction has the most significant influence on the CO₂/MPa ratio. An optimization framework was also implemented to explore the trade-offs among mix components, showing that increasing SCM content does not always lead to improved CO₂/MPa ratio, highlighting the need for balanced mixture design. The developed models offer a practical tool for supporting early-stage decision-making in construction projects by enabling rapid sustainability assessments of concrete mixtures, independent of specific SCM types, production methods, or geographical context.http://www.sciencedirect.com/science/article/pii/S221450952500539XSupplementary Cementitious MaterialsGaussian Process RegressorPermutation Feature ImportanceNSGA-II |
| spellingShingle | Felipe Vargas Iván La Fé-Perdomo Jorge A. Ramos-Grez Ivan Navarrete Machine learning-based estimation of CO2 footprint and environmental-mechanical performance of blended cement concrete Case Studies in Construction Materials Supplementary Cementitious Materials Gaussian Process Regressor Permutation Feature Importance NSGA-II |
| title | Machine learning-based estimation of CO2 footprint and environmental-mechanical performance of blended cement concrete |
| title_full | Machine learning-based estimation of CO2 footprint and environmental-mechanical performance of blended cement concrete |
| title_fullStr | Machine learning-based estimation of CO2 footprint and environmental-mechanical performance of blended cement concrete |
| title_full_unstemmed | Machine learning-based estimation of CO2 footprint and environmental-mechanical performance of blended cement concrete |
| title_short | Machine learning-based estimation of CO2 footprint and environmental-mechanical performance of blended cement concrete |
| title_sort | machine learning based estimation of co2 footprint and environmental mechanical performance of blended cement concrete |
| topic | Supplementary Cementitious Materials Gaussian Process Regressor Permutation Feature Importance NSGA-II |
| url | http://www.sciencedirect.com/science/article/pii/S221450952500539X |
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